[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"nav-categories":3,"article-the-seven-petal-flower-test-chatgpt-s-enduring-symmetry-struggle-and-why-it-s-getting-better":70},{"data":4},[5,37,57,64],{"name":6,"slug":7,"categories":8},"Productivity","productivity",[9,13,17,21,25,29,33],{"id":10,"title":11,"slug":12},17,"Branding","branding",{"id":14,"title":15,"slug":16},19,"Marketing","marketing",{"id":18,"title":19,"slug":20},20,"Work","work",{"id":22,"title":23,"slug":24},34,"Community","community",{"id":26,"title":27,"slug":28},21,"For newbies","for-newbies",{"id":30,"title":31,"slug":32},24,"Investment","investment",{"id":34,"title":35,"slug":36},22,"Finance","finance",{"name":38,"slug":39,"categories":40},"Tech","tech",[41,45,49,53],{"id":42,"title":43,"slug":44},28,"Technology","technology",{"id":46,"title":47,"slug":48},32,"Artificial Intelligence","artificial-intelligence",{"id":50,"title":51,"slug":52},26,"Security and protection","security-and-protection",{"id":54,"title":55,"slug":56},31,"YouTube Blog","youtube-blog",{"name":58,"slug":59,"categories":60},"News","news",[61],{"id":62,"title":58,"slug":63},18,"quasanews",{"name":65,"slug":66,"categories":67},"Business","business",[68],{"id":69,"title":65,"slug":66},16,{"post":71,"published_news":96,"popular_news":156,"categories":215},{"title":72,"description":73,"meta_title":72,"meta_description":74,"meta_keywords":75,"text":76,"slug":77,"created_at":78,"publish_at":79,"formatted_created_at":80,"category_id":26,"links":81,"view_type":86,"video_url":87,"views":34,"likes":88,"lang":89,"comments_count":88,"category":90},"The Seven-Petal Flower Test: ChatGPT’s Enduring Symmetry Struggle (and Why It’s Getting Better)","It’s not a serious benchmark like GPQA or SWE-bench, but it’s delightfully human. It reminds us that even as frontier models crush complex reasoning, basic perceptual tasks can still trip them up in surprising ways.","There’s a beloved Russian fairy tale about a magical Tsvetik-Semitsvetik — a little flower with exactly seven petals","For years, this innocent story has become an unexpected benchmark for testing multimodal AI models.","\u003Cp>There’s a beloved Russian fairy tale about a magical \u003Cstrong>Tsvetik-Semitsvetik\u003C/strong> — a little flower with exactly \u003Cstrong>seven petals\u003C/strong>, each granting a wish.\u003C/p>\n\n\u003Cp>\u003Cpicture>\u003Csource srcset=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212233620.webp\" type=\"image/webp\">\u003Cimg alt=\"The Seven-Petal Flower Test: ChatGPT’s Enduring Symmetry Struggle (and Why It’s Getting Better)\" class=\"image-align-left\" height=\"201\" src=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212233620.jpg\" width=\"300\" />\u003C/picture>For years, this innocent story has become an unexpected benchmark for testing multimodal AI models.\u003C/p>\n\n\u003Cp>\u003Cstrong>The prompt is brutally simple:\u003C/strong>\u003C/p>\n\n\u003Cp>\u003Cem>Draw a flower with exactly seven petals\u003C/em>.”\u003C/p>\n\n\u003Cp>And for the longest time, the answer was almost always… \u003Cstrong>eight\u003C/strong>.\u003C/p>\n\n\u003Chr />\n\u003Ch4>\u003Cstrong>The Classic Failure Loop\u003C/strong>\u003C/h4>\n\n\u003Cp>\u003Cstrong>\u003Cpicture>\u003Csource srcset=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212235704.webp\" type=\"image/webp\">\u003Cimg alt=\"The Seven-Petal Flower Test: ChatGPT’s Enduring Symmetry Struggle (and Why It’s Getting Better)\" class=\"image-align-right\" height=\"447\" src=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212235704.jpg\" width=\"300\" />\u003C/picture>Here’s how the ritual usually goes:\u003C/strong>\u003C/p>\n\n\u003Col>\n\t\u003Cli>User asks ChatGPT (or similar model) to generate a seven-petal flower.\u003C/li>\n\t\u003Cli>It produces a beautiful illustration… with \u003Cstrong>8 petals\u003C/strong>.\u003C/li>\n\t\u003Cli>User: “Please count the petals in this image.”\u003C/li>\n\t\u003Cli>Model confidently replies: “There are exactly seven petals, arranged evenly around the center. Starting from the top and going clockwise: 1, 2, 3, 4, 5, 6, 7.”\u003C/li>\n\t\u003Cli>User uploads the same image back: “Count again.”\u003C/li>\n\t\u003Cli>Model has an existential moment.\u003C/li>\n\u003C/ol>\n\n\u003Cp>\u003Cstrong>In one recent exchange, the model openly admitted its mistakes:\u003C/strong>\u003C/p>\n\n\u003Cp>\u003Cem>“I got carried away by the priming from the original request for seven petals. I performed a quick symmetry-based estimate instead of a careful recount. Upon closer inspection, there are indeed \u003Cstrong>8 petals\u003C/strong>. I was wrong and overconfident.”\u003C/em>\u003C/p>\n\n\u003Cp>\u003Cstrong>It then correctly identified two core errors:\u003C/strong>\u003C/p>\n\n\u003Cul>\n\t\u003Cli>\u003Cstrong>Priming bias\u003C/strong> — the request for seven petals influenced its visual interpretation.\u003C/li>\n\t\u003Cli>\u003Cstrong>Insufficient verification\u003C/strong> — it didn’t double-check the actual pixel output.\u003C/li>\n\u003C/ul>\n\n\u003Chr />\n\u003Ch4>\u003Cstrong>The Redemption Arc\u003C/strong>\u003C/h4>\n\n\u003Cp>\u003Cpicture>\u003Csource srcset=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212231471.webp\" type=\"image/webp\">\u003Cimg alt=\"The Seven-Petal Flower Test: ChatGPT’s Enduring Symmetry Struggle (and Why It’s Getting Better)\" class=\"image-align-left\" height=\"298\" src=\"https://cdn.quasa.io/photos/0001/image-2026-05-06t212231471.jpg\" width=\"200\" />\u003C/picture>When the user asked it to \u003Cstrong>try again\u003C/strong> and generate a version with \u003Cstrong>exactly\u003C/strong> seven petals, the model succeeded. A clean, symmetrical seven-petal flower appeared.\u003C/p>\n\n\u003Cp>This is the interesting part. Earlier versions of these models would stubbornly keep generating eight (or nine) petals no matter how many times you asked.\u003C/p>\n\n\u003Cp>Today, they can:\u003C/p>\n\n\u003Cul>\n\t\u003Cli>Recognize their own counting mistake when confronted with evidence.\u003C/li>\n\t\u003Cli>Reflect on the psychological-like bias that caused it.\u003C/li>\n\t\u003Cli>Correct the generation in the next attempt.\u003C/li>\n\u003C/ul>\n\n\u003Chr />\n\u003Ch4>\u003Cstrong>Why Does This Keep Happening?\u003C/strong>\u003C/h4>\n\n\u003Cp>The root cause is deceptively simple: \u003Cstrong>training data\u003C/strong>.\u003C/p>\n\n\u003Cp>Real flowers in nature and photography rarely have exactly seven petals. Common counts are 5 (roses, buttercups), 6, 8, or irregular numbers. Seven-petal flowers exist but are uncommon, so they’re underrepresented in image-text datasets. When the model tries to generate a “nice symmetrical flower,” it defaults to the most common even or aesthetically pleasing numbers it has seen.\u003C/p>\n\n\u003Cp>Other models show similar quirks. One competitor (affectionately called “Banana” in the community) honestly notes: “I drew you a flower, but it ended up with 9 petals instead of 7. The model made a counting error.” Then it fails to fix it.\u003C/p>\n\n\u003Cp>Also read:\u003C/p>\n\n\u003Cul>\n\t\u003Cli>\u003Ca href=\"https://quasa.io/media/services-the-new-software-why-the-next-1-trillion-company-will-look-like-a-services-firm\">Services: The New Software – Why the Next $1 Trillion Company Will Look Like a Services Firm\u003C/a>\u003C/li>\n\t\u003Cli>\u003Ca href=\"https://quasa.io/media/unmasking-runway-characters-the-unexpected-rise-of-the-real-time-avatar\">Unmasking Runway Characters: The Unexpected Rise of the Real-Time Avatar\u003C/a>\u003C/li>\n\t\u003Cli>\u003Ca href=\"https://quasa.io/media/when-cursor-wiped-a-user-s-pc-a-cautionary-tale-of-ai-overreach\">When Cursor Wiped a User's PC: A Cautionary Tale of AI Overreach\u003C/a>\u003C/li>\n\u003C/ul>\n\n\u003Chr />\n\u003Ch4>\u003Cstrong>What This Tiny Test Actually Reveals\u003C/strong>\u003C/h4>\n\n\u003Cp>\u003Cstrong>The seven-petal flower has become a charming stress test for several capabilities:\u003C/strong>\u003C/p>\n\n\u003Cul>\n\t\u003Cli>Precise visual counting;\u003C/li>\n\t\u003Cli>Symmetry understanding;\u003C/li>\n\t\u003Cli>Resistance to prompt priming;\u003C/li>\n\t\u003Cli>Self-correction and metacognition.\u003C/li>\n\u003C/ul>\n\n\u003Cp>It’s not a serious benchmark like GPQA or SWE-bench, but it’s delightfully human. It reminds us that even as frontier models crush complex reasoning, basic perceptual tasks can still trip them up in surprising ways.\u003C/p>\n\n\u003Cp>Yet the progress is real. The ability to admit “I was wrong because of bias and sloppy checking” — and then fix it — shows genuine improvement in reliability and humility.\u003C/p>\n\n\u003Cp>So the next time you feel like testing a new image model, don’t reach for math problems or coding challenges. Just say:\u003C/p>\n\n\u003Cp>\u003Cem>“Draw the seven-petaled flower.”\u003C/em>\u003C/p>\n\n\u003Cp>If it gets exactly seven petals on the first try… you’ll know the models have truly leveled up.\u003C/p>","the-seven-petal-flower-test-chatgpt-s-enduring-symmetry-struggle-and-why-it-s-getting-better","2026-05-06T19:24:51.000000Z","2026-05-19T09:18:00.000000Z","19.05.2026",{"image":82,"image_webp":83,"thumb":84,"thumb_webp":85},"https://cdn.quasa.io/images/news/iAS4MHMMmZeI9ABzGt3r1At7alQekSok3ePD0Xys.jpg","https://cdn.quasa.io/images/news/iAS4MHMMmZeI9ABzGt3r1At7alQekSok3ePD0Xys.webp","https://cdn.quasa.io/thumbs/news-thumb/images/news/iAS4MHMMmZeI9ABzGt3r1At7alQekSok3ePD0Xys.jpg","https://cdn.quasa.io/thumbs/news-thumb/images/news/iAS4MHMMmZeI9ABzGt3r1At7alQekSok3ePD0Xys.webp","small",null,0,"en",{"id":26,"title":27,"slug":28,"meta_title":91,"meta_description":92,"meta_keywords":93,"deleted_at":87,"created_at":94,"updated_at":95,"lang":89},"Newbie Growth Hacks: AI, Solo Startups & 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